{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f705ed10-4b77-4037-b517-924c1c56fe93",
   "metadata": {},
   "source": [
    "# 6. 分类问题\n",
    "\n",
    "- Sigmoid函数\n",
    "- 交叉熵损失函数\n",
    "- 计算准确率\n",
    "- 一元回归分类\n",
    "- 数据集可视化\n",
    "- 多元回归分类\n",
    "- Softmax函数\n",
    "- 多分类交叉熵损失函数\n",
    "- 多分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f20875-c50d-46df-906b-49ec1d0c6002",
   "metadata": {},
   "source": [
    "# 6.1 实现Sigmoid函数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "假设有一组线性输出X：-10.,-9.5,-9.,-8.5,-8.,-7.5,-7.,-6.5,-6.,-5.5,-5.,-4.5,-4.,-3.5,\n",
    "-3.,-2.5,-2.,-1.5,-1.,-0.5,0., 0.5,1., 1.5,2., 2.5,3.,3.5,4.,4.5,5., 5.5,6., 6.5,7., 7.5,8., 8.5,9., 9.5,10,\n",
    "69.65,75.69,95.30\n",
    "\n",
    "要求：\n",
    "\n",
    "- 使用Sigmoid函数，对线性结果进行转换，得到转换结果Y。\n",
    "- 以X为x轴数据，以Y为y轴数据，绘制Sigmoid函数的曲线。。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5b4fc39-cbcf-432a-bf1e-e75e642d4b87",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b624ebee-980f-4c1e-b963-a24ff0b669f6",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "要计算$e^{-(W^TX)}$，可以使用tf.exp方法来计算的指数，其代码格式如下："
   ]
  },
  {
   "cell_type": "raw",
   "id": "8d53c5ec-c767-4d3f-b5bb-cde5a939b668",
   "metadata": {},
   "source": [
    "tf.exp(-(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[-10.   -9.5  -9.   -8.5  -8.   -7.5  -7.   -6.5  -6.   -5.5  -5.   -4.5\n",
      "  -4.   -3.5  -3.   -2.5  -2.   -1.5  -1.   -0.5   0.    0.5   1.    1.5\n",
      "   2.    2.5   3.    3.5   4.    4.5   5.    5.5   6.    6.5   7.    7.5\n",
      "   8.    8.5   9.    9.5  10. ], shape=(41,), dtype=float64)\n",
      "tf.Tensor(\n",
      "[4.53978687e-05 7.48462275e-05 1.23394576e-04 2.03426978e-04\n",
      " 3.35350130e-04 5.52778637e-04 9.11051194e-04 1.50118226e-03\n",
      " 2.47262316e-03 4.07013772e-03 6.69285092e-03 1.09869426e-02\n",
      " 1.79862100e-02 2.93122308e-02 4.74258732e-02 7.58581800e-02\n",
      " 1.19202922e-01 1.82425524e-01 2.68941421e-01 3.77540669e-01\n",
      " 5.00000000e-01 6.22459331e-01 7.31058579e-01 8.17574476e-01\n",
      " 8.80797078e-01 9.24141820e-01 9.52574127e-01 9.70687769e-01\n",
      " 9.82013790e-01 9.89013057e-01 9.93307149e-01 9.95929862e-01\n",
      " 9.97527377e-01 9.98498818e-01 9.99088949e-01 9.99447221e-01\n",
      " 9.99664650e-01 9.99796573e-01 9.99876605e-01 9.99925154e-01\n",
      " 9.99954602e-01], shape=(41,), dtype=float64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 构造线性输出结果\n",
    "X=tf.range(start=-10,limit=10.5,delta=0.5,dtype=tf.float64)\n",
    "print(X)\n",
    "\n",
    "# 使用Sigmoid函数对线性结果进行转换，得到Y\n",
    "Y=1/(1+tf.exp(-(X)))\n",
    "print(Y)\n",
    "# 可视化\n",
    "plt.figure(figsize=(6,4))\n",
    "# 绘制直线\n",
    "plt.plot(X, Y, color=\"green\", label=\"sigmoid\", linewidth=1)\n",
    "plt.xlabel(\"线性结果X\",fontsize=14)\n",
    "plt.ylabel(\"Sigmoid输出Y\",fontsize=14)\n",
    "# x轴刻度范围\n",
    "plt.xlim(-12,12)\n",
    "# y轴刻度范围\n",
    "plt.ylim(-0.2,1.2)\n",
    "plt.suptitle(\"Sigmoid函数\",fontsize=20)\n",
    "# 显示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "947bd149-004c-4238-863f-ba125a57b79b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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